Analyzer

ABSTRACT

An analyzer includes a preliminary analysis unit that extracts interval data obtained by cutting out time-series data with a predetermined sliding window width, and analyzes simplicity of a change trend of the extracted interval, a data division unit that divides the data into pieces of division data with a sliding window width set based on an analysis result performed by the preliminary analysis unit, a data generation unit that generates combination data which is a text indicating a change trend in the division data based on the division data, and a data analysis unit that analyzes the combination data.

RELATED APPLICATIONS

The present application claims priority to Japanese Patent ApplicationNumber 2019-202079 filed Nov. 7, 2019, the disclosure of which is herebyincorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to an analyzer.

2. Description of the Related Art

A device that monitors an operation state of an industrial machine suchas a robot or a machine tool installed on a manufacturing line, andmanages the operation state of the industrial machine has beenintroduced at a manufacturing site such as a factory.

The device that manages the operation state of the industrial machinemonitors, for example, time-series data such as a position, a speed, ora torque of a motor detected in each industrial machine via a network,data indicating a change in a signal at a predetermined time, andtime-series data such as vibration, sound, or a moving image detected bya sensor attached to the industrial machine. The device that manages theoperation state of the industrial machine detects a trend indicating anabnormality from a change state of each data with a time shift (forexample, JP 2019-144931 A, JP 2019-012473 A).

The operation state of the industrial machine may need to be determinedbased on a relationship between pieces of data rather than based onpredetermined single data. For example, the operation state may bedetermined by analyzing the relationship between the pieces of data suchas whether or not two drive systems provided in the industrial machineare moving up or down at the same timing or do not have any relation, orwhether or not a predetermined signal is turned on or off when a certainpart is in a specific state. In this case, it is necessary to grasp therelationship between the pieces of data in advance and then create analgorithm or a condition for the determination.

When the number of pieces of data acquired from the industrial machineis small or when a data acquisition period is relatively short, thepieces of data are displayed on a graph on the same time scale, andthus, an operator can visually grasp the relationship between the piecesof data while seeing the display. However, when the number of pieces ofdata acquired from the industrial machine is large or when the dataacquisition period is relatively long, it is difficult for a person todetermine the relationship between the pieces of data.

Thus, a method of analyzing the relationship between the pieces of datais needed. In general, a degree of similarity between pieces oftime-series numerical data can be calculated by Euclidean distance,cross-correlation function (CCF), dynamic time warping (DTW), or thelike. However, even if these methods are used, it is difficult tosimultaneously grasp a relationship between a plurality of pieces ofdata in each part by paying attention to a characteristic part of thedata acquired over a long period.

Thus, there is a demand for a mechanism that can easily analyze arelationship between a plurality of pieces of time-series data.

SUMMARY OF THE INVENTION

The present invention solves the above-mentioned problems by enablingapplication of a technology of association analysis which is a knowndata analysis method of grasping of a relationship between a pluralityof pieces of data including time-series data. In general, theassociation analysis cannot be applied to the time-series data, but ananalyzer according to an aspect of the present invention can apply theassociation analysis by finding and extracting an interval having acharacteristic relationship between pieces of time-series data fromthese pieces of acquirable time-series data.

An aspect of the present invention is an analyzer that analyzes dataincluding time-series data acquired from an industrial machine. Theanalyzer includes a preliminary analysis unit configured to extractinterval data obtained by cutting out the time-series data included inthe data with a predetermined sliding window width, and analyzesimplicity of a change trend of the extracted interval data, a datadivision unit configured to divide the data into pieces of division datawith a sliding window width set based on an analysis result performed bythe preliminary analysis unit, a data generation unit configured togenerate combination data which is a text indicating a change trend inthe division data based on the division data, and a data analysis unitconfigured to analyze the combination data.

Another aspect of the present invention is an analysis method ofanalyzing data including time-series data acquired from an industrialmachine. The analysis method includes a first step of extractinginterval data obtained by cutting out the time-series data included inthe data with a predetermined sliding window width, and analyzingsimplicity of a change trend of the extracted interval data, a secondstep of setting a sliding window width based on an analysis result inthe first step, and dividing the data into pieces of division data witha sliding window width set, a third step of generating combination datawhich is a text indicating a change trend in the division data based onthe division data, and a fourth step of analyzing the combination data.

According to the aspect of the present invention, a relationship betweena plurality of pieces of data including time-series data can be grasped,and data can be more easily utilized.

BRIEF DESCRIPTION OF THE DRAWINGS

Objects and features of the present invention will become apparent fromthe following description of embodiments with reference to theaccompanying drawings. Of those drawings:

FIG. 1 is a schematic hardware configuration diagram of an analyzeraccording to an embodiment;

FIG. 2 is a schematic functional block diagram of an analyzer accordingto a first embodiment;

FIG. 3 is a diagram illustrating an example of data stored in anacquired data storage unit;

FIG. 4 is a diagram for describing determination of monotonicity;

FIG. 5 is a diagram illustrating a display example of the number ofnon-monotonicity intervals and the number of states;

FIG. 6 is a schematic functional block diagram of an analyzer accordingto a second embodiment; and

FIG. 7 is a schematic functional block diagram of an analyzer accordingto a third embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, embodiments of the present invention will be described withreference to the drawings.

FIG. 1 is a schematic hardware configuration diagram illustrating ananalyzer according to an embodiment of the present invention. Ananalyzer 1 may be implemented in, for example, a controller thatcontrols an industrial machine. The analyzer 1 may be implemented in apersonal computer installed with the controller that controls theindustrial machine, or a personal computer, a cell computer, a fogcomputer, or a cloud server connected to the controller via a wired orwireless network. In the present embodiment, an example in which theanalyzer 1 is implemented in a personal computer connected to thecontroller that controls the industrial machine via the network isdescribed.

A central processing unit (CPU) 11 included in the analyzer 1 accordingto the present embodiment is a processor that controls the analyzer 1 asa whole. The CPU 11 reads a system program stored in a read only memory(ROM) 12 via a bus 22. The CPU 11 controls the entire analyzer 1according to the system program. A random access memory (RAM) 13temporarily stores calculation data, display data, various kinds of datainput from the outside, and the like.

A nonvolatile memory 14 is, for example, a memory backed up by a battery(not illustrated), a solid state drive (SSD), or the like. Thenonvolatile memory 14 maintains a storage state even if a power of theanalyzer 1 is turned off. Data read from an external device 72 via aninterface 15, data input via an input device 71, data acquired from acontroller 3 via an interface 20, and the like are stored in thenonvolatile memory 14. The data stored in the nonvolatile memory 14 maybe loaded in the RAM 13 in the case of being executed or used. Varioussystem programs such as a known analysis program are written in the ROM12 in advance.

The interface 15 is an interface for connecting the CPU 11 of theanalyzer 1 to the external device 72 such as a Universal Serial Bus(USB) device. For example, data or the like acquired by anotherindustrial machine may be read from the external device 72 side. Thedata or the like processed in the analyzer 1 may be stored in anexternal storage unit via the external device 72.

The interface 20 is an interface for connecting the CPU 11 of theanalyzer 1 to a wired or wireless network 5. The controller 3, the fogcomputer, the cloud server, and the like are connected to the network 5,and exchange data with the analyzer 1.

The data read on the memory, the data obtained as a result obtained byexecuting the program, and the like are output to a display device 70via an interface 17 and displayed thereon. The input device 71 includinga keyboard, and a pointing device delivers commands, data, and the likebased on an operation of an operator to the CPU 11 via an interface 18.

FIG. 2 is a schematic block diagram illustrating functions of theanalyzer 1 according to a first embodiment. The functions of theanalyzer 1 according to the present embodiment are realized by the CPU11 of the analyzer 1 illustrated in FIG. 1 executing the system programand controlling operations of the units of the analyzer 1.

The analyzer 1 includes a data acquisition unit 100, a preliminaryanalysis unit 110, a display unit 120, a preprocessing unit 130, and adata analysis unit 140. In the RAM 13 or the nonvolatile memory 14 ofthe analyzer 1, an acquired data storage unit 200 is prepared in advanceas a region for storing the data acquired from the input device 71, theexternal device 72, the controller 3, or the like. In the RAM 13 or thenonvolatile memory 14, an analysis condition storage unit 210 as aregion for storing analysis conditions of data and a combination datastorage unit 220 as a region for storing a result obtained bypreprocessing data are prepared in advance.

The data acquisition unit 100 is realized by the CPU 11 executing thesystem program read from the ROM 12 and by the CPU 11 mainly performingarithmetic processing using the RAM 13 and the nonvolatile memory 14 andinput control processing using the interface 15, 18 or 20. The dataacquisition unit 100 acquires time-series data detected in operation ofan industrial machine 4, data indicating a change of a signal at apredetermined time, and the like. The data indicating the change of thesignal at the predetermined time may be handled as the time-series databy representing the change of the signal on a time axis. The dataacquisition unit 100 acquires position data, speed data, accelerationdata, and torque data of a motor of the industrial machine 4. The dataacquisition unit 100 acquires vibration data, sound data, and image datadetected by a sensor 6 attached to the industrial machine 4. The dataacquisition unit 100 acquires various kinds of data and the like storedin the controller 3 that controls the industrial machine 4. The dataacquisition unit 100 may directly acquire data related to the industrialmachine 4 from the controller 3 via the network 5. The data acquisitionunit 100 may acquire data already acquired by and stored in the externaldevice 72, the fog computer, the cloud server (not illustrated), or thelike. The data acquired by the data acquisition unit 100 is stored inthe acquired data storage unit 200.

The preliminary analysis unit 110 is realized by the CPU 11 executingthe system program read from the ROM 12 and by the CPU 11 mainlyperforming the arithmetic processing using the RAM 13 and thenonvolatile memory 14. The preliminary analysis unit 110 performspreliminary analysis on the data stored in the acquired data storageunit 200. The preliminary analysis unit 110 outputs the analysis resultto the display unit 120. The preliminary analysis is performed in orderto demonstrate to the operator a method of defining an interval of thedata stored in the acquired data storage unit 200 such that intervaldata indicating a simple change trend can be extracted. That is, thepreliminary analysis is performed in order to analyze, in advance, thesimplicity of the change trend of the interval data when the data isdivided into predetermined intervals. The preliminary analysis unit 110includes an interval extraction unit 112, a non-monotonicity intervalnumber calculation unit 114, and a state number calculation unit 116.

The interval extraction unit 112 extracts interval data obtained bycutting out the data stored in the acquired data storage unit 200 bysliding windows S1 to Si each having a sliding window width Sw based ona value of the sliding window width Sw stored in the analysis conditionstorage unit 210. The interval extraction unit 112 arranges a left endof the sliding window S1 at a position of time t=0. The intervalextraction unit 112 arranges the sliding windows such that a left end ofthe succeeding sliding window is at a position of a right end of thepreceding sliding window. The interval extraction unit 112 extracts theinterval data by cutting out the time-series data by sliding windows.The sliding window width Sw may be set as the analysis condition by theoperator operating the input device 71. FIG. 3 illustrates an example ofthe data stored in the acquired data storage unit 200. In the example ofFIG. 3, data acquired when machined components are machined by a machinetool as the industrial machine 4 is illustrated. For example, it isassumed that the acquired data storage unit stores time-series data suchas the number of machined components machined by the machine tool, aservo motor temperature and a spindle motor temperature of the machinetool. It is assumed that a signal state of an M01 decode signal, asignal state of a cutting feed signal, and a signal state of a singleblock confirmation signal are stored as signal states of the machinetool in the acquired data storage unit. At this time, the intervalextraction unit 112 arranges these pieces of data on the time axis, andextracts, as the interval data, data in each sliding window when thetime axis is divided according to the preset sliding window width Sw. Arelationship between the data and the sliding window width Swillustrated in FIG. 3 may be displayed on the display device 70 by thedisplay unit 120. By doing so, the operator can adjust the slidingwindow width Sw while confirming the relationship between each data andthe sliding window width Sw on a screen. Although the interval data iscut out such that a time at the left end of the sliding window S1becomes zero, this position may be adjustable. In this case, the amountof adjusted position ew is also stored in the analysis condition storageunit 210 together with the sliding window width Sw.

The non-monotonicity interval number calculation unit 114 obtains, forthe interval data which is numerical data among the pieces of intervaldata extracted by the interval extraction unit 112, the number of piecesof interval data in which a change in the numerical value indicatesnon-monotonicity (no monotonicity). The non-monotonicity interval numbercalculation unit 114 determines that the interval data which is thenumerical data indicates non-monotonicity when the interval data doesnot indicate a trend of a monotonic increase or a monotonic decrease.The determination at this time may be allowed to have some margin. Forexample, as illustrated in FIG. 4, a straight line connecting a startpoint value Ds and an end point value De is obtained for certaininterval data D. When there is a portion at which a difference between avalue at each point in time of the interval data D and a value withwhich the straight line is obtained at each point in time exceeds apredetermined threshold value, the non-monotonicity interval numbercalculation unit 114 may determine that the interval data D indicatesnon-monotonicity. In a similar case, when there is a portion at which aratio of the value at each point in time of the interval data D and thevalue with which the straight line is obtained at each point in timeexceeds the predetermined threshold value, the non-monotonicity intervalnumber calculation unit 114 may determine that the interval data Dindicates non-monotonicity. When there are simultaneously an intervalhaving a predetermined width (for example, a width of 30% of theinterval data D) and a continuous positive slope equal to or larger thana predetermined positive threshold value and an interval having acontinuous negative slope equal to or less than a negative thresholdvalue within a certain interval data D, the non-monotonicity intervalnumber calculation unit 114 may determine that the interval data Dindicates non-monotonicity. When it is generally determined that theinterval data indicates non-monotonicity according to characteristics ofthe data, the non-monotonicity interval number calculation unit 114 maydetermine that the interval data indicates non-monotonicity. The valueobtained by the non-monotonicity interval number calculation unit 114may be used as a scale for measuring the simplicity of the change trendof the interval data in the numerical data.

The state number calculation unit 116 calculates, for the interval datathat is not the numerical data such as a signal among the pieces ofinterval data extracted by the interval extraction unit 112, the numberof states of the data in each piece of interval data. The state numbercalculation unit 116 obtains the number of pieces of interval data foreach calculated number of states. For example, when the interval datadoes not change with an ON or OFF value in the interval, the statenumber calculation unit 116 calculates the number of states of theinterval data as 1. For example, when the interval data changes oncefrom the value of ON to the value of OFF or from the value of OFF to thevalue of ON in the interval, the state number calculation unit 116calculates the number of states of the interval data as 2. For example,when the interval data changes from the value of ON to the value of OFFin the interval and then changes from the value of OFF to the value ofON or when the interval data changes from the value of OFF to the valueof ON and then changes from the value of ON to the value of OFF, thatis, when the interval data changes twice, the state number calculationunit 116 calculates the number of states of the interval data as 3. Thevalue calculated by the state number calculation unit 116 can be used asa scale for measuring the simplicity of the change trend of the intervaldata in data other than the numerical data.

The display unit 120 is realized by the CPU 11 executing the systemprogram read from the ROM 12 and by the CPU 11 mainly performing thearithmetic processing using the RAM 13 and the nonvolatile memory 14 andoutput control processing using the interface 17. The display unit 120displays a result of the preliminary analysis of the preliminaryanalysis unit 110 and a result of the data analysis of the data analysisunit 140 on the display device 70. The display unit 120 displays therelationship between the data and the sliding window width Swillustrated in FIG. 3, for example. The display unit 120 displays, asthe result of the preliminary analysis of the preliminary analysis unit110 in the sliding window width Sw set by the operator, the number ofpieces of interval data indicating non-monotonicity obtained by thenon-monotonicity interval number calculation unit 114. The display unit120 displays the number of pieces of interval data for each number ofstates obtained by the state number calculation unit 116.

FIG. 5 illustrates an example of a display on the display unit 120. Inthe example of FIG. 5, the number of pieces of interval data indicatingnon-monotonicity obtained by the non-monotonicity interval numbercalculation unit 114 and the number of pieces of interval data for eachnumber of states obtained by the state number calculation unit 116 aredisplayed together with the relationship between each data stored in theacquired data storage unit 200 and the sliding window width Sw. Theoperator can examine the validity of the set sliding window width Swwhile seeing such a display. The operator can examine a value of thesliding window width Sw with less non-monotonicity intervals and lessintervals at which the number of states is large while seeing thedisplay illustrated in FIG. 5.

The preprocessing unit 130 is realized by the CPU 11 executing thesystem program read from the ROM 12 and by the CPU 11 mainly performingthe arithmetic processing using the RAM 13 and the nonvolatile memory14. The preprocessing unit 130 performs preprocessing on the data storedin the acquired data storage unit 200. The preprocessing unit 130 storesthe combination data generated as a result of the preprocessing in thecombination data storage unit 220. In the preprocessing executed by thepreprocessing unit 130, the data stored in the acquired data storageunit 200 is divided according to the sliding window width Sw determinedto be valid by the preliminary analysis, and the combination data isgenerated by converting into a predetermined text indicating the changetrend of the divided division data. The preprocessing unit 130 includesa data division unit 132 and a data generation unit 134.

The data division unit 132 creates the division data by dividing thedata stored in the acquired data storage unit 200 according to thesliding windows S1 to Si each having the sliding window width Sw basedon the value of the sliding window width Sw stored in the analysiscondition storage unit 210. The sliding window width Sw stored in theanalysis condition storage unit 210 is the value determined to be validby the operator by the preliminary analysis using the preliminaryanalysis unit 110. The sliding window width Sw used for creating thedivision data may be one value determined to be the most valid by theoperator, or may be a plurality of values determined by the operator.The sliding window is used for cutting out the interval data with thetime 0 as a reference position, but the reference position may beadjustable. The positions of the sliding windows S1 to Si may beadjustable. In this case, the position of the sliding window is adjustedby using the sliding window width Sw and the amount of adjusted positionew stored in the analysis condition storage unit 210.

The data generation unit 134 generates, for the division data created bythe data division unit 132, the combination data that is a text in apredetermined format indicating the change trend of the division data.When the division data is the numerical data, the data generation unit134 converts the division data into a text indicating an increased ordecreased value from a start point value to an end point value. The datageneration unit 134 generates the combination data including theconverted text. For example, when the division data is the data of theservo motor temperature and the value increases by 5° C. from the startpoint to the end point of the interval, the data generation unit 134generates the combination data “servo motor temperature (+5° C.)” fromthe division data. For example, when the division data is the data ofthe spindle motor temperature and the value decreases by 8° C. from thestart point to the end point of the interval, the data generation unit134 generates the combination data “spindle motor temperature (−8° C.)”from the division data.

When the division data is data other than the numerical data, the datageneration unit 134 converts the division data into a text indicating aflow of a change in a state of the division data. The data generationunit 134 generates the combination data including the converted text.For example, when the division data is the data of the cutting feedsignal and does not change with the value of ON in the interval, thedata generation unit 134 generates the combination data “cutting feedsignal ON” from the division data. For example, when the division datais the data of the single block confirmation signal and changes from thevalue of OFF to the value of ON in the interval and then changes fromthe value of ON to the value of OFF, the data generation unit 134generates the combination data “single block confirmation signalOFF->ON->OFF” from the division data.

The data analysis unit 140 is realized by the CPU 11 executing thesystem program read from the ROM 12 and by the CPU 11 mainly performingthe arithmetic processing using the RAM 13 and the nonvolatile memory14. The data analysis unit 140 performs known association analysis basedon the combination data stored in the combination data storage unit 220.The association analysis is an analysis method of finding apredetermined pattern (correlation rule) from a set of data. Forexample, in the association analysis, a correlation rule that there is acorrelation between “servo motor temperature increases” and “spindlemotor temperature increases” is found. Since the data acquired by thedata acquisition unit 100 includes the time-series data, it is difficultto apply a general association analysis to this data. However, in theanalyzer 1 according to the present invention, the combination datawhich is the text indicating the data type and the change trend of thedata is generated when the preprocessing of the preprocessing unit 130is performed. Thus, a correlation between a change of the numerical dataand a change in the state of the signal value for example can beanalyzed by applying the method of the association analysis to thiscombination data. When the association analysis is performed, the dataanalysis unit 140 regards the combination data generated based on thedata detected at the same time as (co-occurrence) combination datasimultaneously occurred. The details of the association analysis havebeen sufficiently known before the present application is filed, andthus, the description thereof is omitted in the present specification.

The data analysis unit 140 may perform the association analysis on thecombination data as it is, or may also perform the association analysisafter a plurality of pieces of combination data is regarded as beingequal based on a predetermined condition, for example. In this case, thedata analysis unit 140 may regard the plurality of pieces of combinationdata as being equal based on an equivalence condition stored in theanalysis condition storage unit 210. For example, when an equivalencecondition that the temperature which is the numerical data is roundeddown to an integer is set as the equivalence condition, the dataanalysis unit 140 performs the association analysis by regarding thepieces of combination data “servomotor temperature: +5.2° C.” and“servomotor temperature: +5.8° C.” as being equal to “servo motortemperature: +5.0° C.”. For example, when the single block confirmationsignal which is data other than the numerical data has three or morestates, an equivalence condition that the state change is regarded asbeing equal to “other” may be set as the equivalence condition. In thiscase, the data analysis unit 140 performs the association analysis byregarding the data of the single block confirmation signal having threeor more states such as the combination data “single block confirmationsignal: OFF->ON->OFF” and the combination data “single blockconfirmation signal: ON->OFF->ON” as being equal to “single blockconfirmation signal: other”. For the equivalence condition, a range inwhich the pieces of combination data are regarded as being equal may bedetermined by the operator based on the characteristics of the data andthe like so as to have an appropriate value. For the equivalencecondition, a range in which the pieces of data are regarded as beingequal may be determined based on a range of the pieces of the data suchas X % of a difference between a maximum value and a minimum value ofthe data.

The data analysis unit 140 may analyze the combination data by otheranalysis methods, in addition to the association analysis, which aredifficult to apply to the time-series data but are applicable to thedata such as the text. For example, a known analysis method foranalyzing an event such as basket analysis, factor analysis, or ABCanalysis may be performed.

The analyzer 1 having the above-described configuration according to thepresent embodiment can analyze a relationship between various kinds oftime-series data acquired from the industrial machine by a knownanalysis method. Thus, it becomes easier to utilize the data. Therelationship between pieces of data obtained by the association analysisperformed by the analyzer 1 according to the present embodiment can beutilized for various purposes.

For example, the relationship between the pieces of data can be utilizedfor stopping the acquisition of unnecessary data. When it is found as aresult of the analysis that values of two or more pieces of data arealmost equal or when it is determined that the signal is turned on in acertain state, these pieces of data can be regarded as duplicate data.Thus, it is possible to examine the stop of the acquisition of any data.Accordingly, it is possible to examine a decrease in a load of a networkused as a data collection device or a data collection path and adecrease in a capacity of a database which is a collection destinationof data.

The relationship between the pieces of data can be also utilized forcomplementing when data is missing. When a relationship between two ormore pieces of data is determined as a result of the analysis, even ifone piece of data is missing and cannot be collected, the relationshipcan be complemented with a value predicted by using a value of the otherdata related to this data.

The relationship between the pieces of data may be utilized forimproving the quality of machining. When a relationship between amachining result (non-defective product or defective product) and datacollected in the case of machining is determined as a result of theanalysis, it is possible to prevent a defective product from beingproduced by specifying a cause of a defect. The machining can be stoppedwhen a sign that the defective product is to be produced is detectedfrom the data collected while the machine is being operated.

The relationship between the pieces of data can also be utilized fordetecting an abnormality. For example, when a relationship of “as theservo temperature increases by 5 degrees, the spindle temperature alsoincreases by 5 degrees” is determined as a result of the analysis, it ispossible to determine that an abnormality occurs if the spindletemperature does not increase when the servo temperature increases by 5degrees.

The relationships between the pieces of data can be utilized for findinga sign of a failure or an alarm. When a relationship between values ofthe signals in a normal operation is determined as a result of theanalysis, for example, and the relationship between the values of thesignals is different from that in the normal operation before the alarmoccurs, it is possible to determine that this relationship is the signof the failure or the alarm.

The relationship between the signals is obtained as a graph byperforming network analysis on the relationship between the pieces ofdata, and thus, it is possible to grasp the relationship between all thesignals. By doing so, it is possible to visually grasp the relationshipbetween all the signals which cannot be found in the associationanalysis alone. Using the knowledge of the relationship obtained bygrasping the relationship between all the signals enables easyextraction of a feature value of the data to be used in, for example,machine learning. It is possible to expect that other utilizationmethods are easily implemented by visually grasping the relationshipbetween all the signals.

FIG. 6 is a schematic block diagram illustrating functions of theanalyzer 1 according to a second embodiment of the present invention.The functions of the analyzer 1 according to the present embodiment arerealized by the CPU 11 of the analyzer 1 illustrated in FIG. 1 executingthe system program and controlling operations of the units of theanalyzer 1.

The analyzer 1 according to the present embodiment is different from theanalyzer 1 according to the first embodiment in that the preprocessingunit 130 includes an equivalence interval conversion unit 136.

The equivalence interval conversion unit 136 classifies the combinationdata generated by the data generation unit 134 into a set regarded to beequal based on the equivalence condition stored in the analysiscondition storage unit 210, and stores the classified combination datain the combination data storage unit 220. The classified combinationdata is analyzed by the data analysis unit 140.

FIG. 7 is a schematic block diagram illustrating functions of theanalyzer 1 according to a third embodiment of the present invention. Thefunctions of the analyzer 1 according to the present embodiment arerealized by the CPU 11 of the analyzer 1 illustrated in FIG. 1 executingthe system program and controlling operations of the units of theanalyzer 1.

The analyzer 1 of the present embodiment is different from the analyzer1 according to the first embodiment in that a sliding window widthadjustment unit 150 is provided.

The sliding window width adjustment unit 150 adjusts the sliding windowwidth Sw based on a predetermined rule, and searches for the slidingwindow width Sw with which the number of pieces of interval dataindicating non-monotonicity obtained by the non-monotonicity intervalnumber calculation unit 114 and the number of pieces of interval datafor each number of states obtained by the state number calculation unit116 are valid. For example, the sliding window width adjustment unit 150sets a sliding window width Sw-j×dw to a sliding window width Sw+j×dwincreased and decreased by a predetermined step with a predeterminedadjustment amount dw with the sliding window width Sw set to theanalysis condition storage unit 210 by the operator as a reference. Thesliding window width adjustment unit 150 may search for the slidingwindow width satisfying a predetermined validity condition by comparingthe number of pieces of interval data indicating non-monotonicityobtained by the non-monotonicity interval number calculation unit 114with the number of pieces of interval data for each number of statesobtained by the state number calculation unit 116 at the time when eachsliding window width is set. The sliding window width adjustment unit150 shifts a division position of the time-series data using the slidingwindow by a predetermined step with a predetermined adjustment amountewd. The sliding window width adjustment unit 150 may search for theamount of adjusted sliding window satisfying a predetermined validitycondition by comparing the number of pieces of interval data indicatingnon-monotonicity obtained by the non-monotonicity interval numbercalculation unit 114 with the number of pieces of interval data for eachnumber of states obtained by the state number calculation unit 116 atthe time when the position of each sliding window is set.

The analyzer 1 according to the present embodiment can automaticallyobtain a better sliding window width and the amount of adjusted positionof the sliding window to some extent. Thus, it is possible to reduce alabor related to an examination work of the sliding window width or theamount of adjusted position of the sliding window by the operator tosome extent.

Although the embodiments of the present invention have been describedabove, the present invention is not limited to the examples of theabove-described embodiments, and can be implemented in various aspectsby appropriately changing the embodiments.

The invention claimed is:
 1. An analyzer that analyzes a relationship between a plurality of pieces of data including time-series data acquired during operation of an industrial machine, the analyzer comprising: a processor configured to: extract interval data obtained by cutting out the time-series data included in the acquired within a predetermined sliding window width, wherein the sliding window width is set as a value by an operator, and analyze a change trend of the extracted interval data; divide the acquired data into pieces of division data with the sliding window width set by the operator; generate combination data including a text of information indicating a change trend in the division data; and analyze the combination data to determine a pattern of correlation in the time-series data to provide the relationship between the plurality of pieces of data, wherein the relationship is used to detect an occurrence of an event in components of the industrial machine, and wherein a result of the relationship is output to a controller for adjusting operation of the industrial machine based on the result of the relationship.
 2. The analyzer according to claim 1, wherein the processor is further configured to obtain a number of pieces of interval data in which a change in a numerical value indicates non-monotonicity among the pieces of interval data which is numerical data.
 3. The analyzer according to claim 1, wherein the processor is further configured to calculate a number of states of each piece of interval data for the interval data other than the numerical data, and obtain the number of pieces of interval data for each calculated number of states.
 4. The analyzer according to claim 1, wherein the processor is further configured to generate the combination data including a text indicating an increased or decreased value from a start point value to an end point value of the division data based on the division data which is the numerical data.
 5. The analyzer according to claim 1, wherein the processor is further configured to generate the combination data including a text indicating a flow of a change of a state of the division data based on the division data other than the numerical data.
 6. An analysis method of analyzing a relationship between a plurality of pieces of data including time-series data acquired during operation of an industrial machine, the method comprising: extracting interval data obtained by cutting out the time-series data included in the acquired time-series data within a predetermined sliding window width, wherein the sliding window width is set as a value by an operator, and analyzing change trend of the extracted interval data; dividing the acquired data into pieces of division data with the sliding window width set by the operator; generating combination data including a text of information indicating a change trend in the division data; analyzing the combination data to determine a pattern of correlation in the time-series data to provide the relationship between the plurality of pieces of data; and outputting a result of the relationship to a controller for adjusting operation of the industrial machine based on the result of the relationship. 